README.md

---

license: cc-by-4.0
task_categories:
  - reinforcement-learning
  - tabular-classification
language:
  - en
tags:
  - synthetic
  - finance
  - defi
  - crypto
  - mcts
  - trading-agents
  - execution-intelligence
pretty_name: Solstice ARC-T DeFi Execution Intelligence
---


# Solstice ARC-T DeFi Execution Intelligence (Sample)

**Synthetic risk-to-execution decision loops for autonomous DeFi agents.** This dataset captures 3,000 multi-step decision traces from autonomous trading agents operating in a simulated Decentralized Finance (DeFi) ecosystem.

Built by [Solstice AI Studio](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic — no real wallet addresses or on-chain history used.

## What's in the box
Each record in the JSONL stream represents a full execution loop, including:
- **Market State:** Simulated oracle prices, liquidity depths, and volatility triggers.
- **Risk Assessment:** Whale movement signals and oracle staleness metrics.
- **Agent Reasoning:** MCTS-style strategy selection (Monte Carlo Tree Search) with success/failure probability weights.
- **Execution Outcome:** Final trade result including gas costs, slippage, and PnL.

## Use Cases
- **Autonomous Agent Training:** Train models to select optimal trading strategies based on simulated market stress.
- **Risk Model Evaluation:** Benchmark protocol safety against extreme market scenarios and oracle failures.
- **Anomaly Detection:** Identify malicious or inefficient trading patterns in high-frequency DeFi telemetry.

## Data Provenance
Generated using Solstice’s PhantasOS / SIMA simulation engine. The simulation uses MCTS (Monte Carlo Tree Search) to explore the decision space of a trading agent, recording both the chosen path and the explored alternatives.

## Get the Full Pack
Scale this dataset to 1M+ decision loops, including cross-chain bridge logic and complex liquidity provider (LP) scenarios.
[www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets)